Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-40.00 19.60 23.50 23.59 27.80 56.00 60089
summary(met$elev)
Min. 1st Qu. Median Mean 3rd Qu. Max.
-13.0 101.0 252.0 415.8 400.0 9999.0
summary(met$wind.sp)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 2.10 2.46 3.60 36.00 79693
Anomalies: The minimum temperature recorded in this dataset is -40, the maximum elevation recorded is 9999, and the wind speed data has 79693 NA values.
Fixes: 1. Replacing all 9999 elevations (impossible) with NA
met$elev[met$elev==9999.0] <-NAsummary(met$elev)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-13 101 252 413 400 4113 710
Now, the highest weather station is at an elevation of 4113 m.
Minimum temperature of -40C looks suspiscious and observations of -40 degC temperatures should be removed.
met <- met[met$temp >-40, ]met2 <-met[order(met$temp), ]head(met2)
USAFID WBAN year month day hour min lat lon elev wind.dir
1203053 722817 3068 2019 8 1 0 56 38.767 -104.3 1838 190
1203055 722817 3068 2019 8 1 1 56 38.767 -104.3 1838 180
1203128 722817 3068 2019 8 3 11 56 38.767 -104.3 1838 NA
1203129 722817 3068 2019 8 3 12 56 38.767 -104.3 1838 NA
1203222 722817 3068 2019 8 6 21 56 38.767 -104.3 1838 280
1203225 722817 3068 2019 8 6 22 56 38.767 -104.3 1838 240
wind.dir.qc wind.type.code wind.sp wind.sp.qc ceiling.ht ceiling.ht.qc
1203053 5 N 7.2 5 NA 9
1203055 5 N 7.7 5 NA 9
1203128 9 C 0.0 5 NA 9
1203129 9 C 0.0 5 NA 9
1203222 5 N 2.6 5 NA 9
1203225 5 N 7.7 5 NA 9
ceiling.ht.method sky.cond vis.dist vis.dist.qc vis.var vis.var.qc
1203053 9 N NA 9 N 5
1203055 9 N NA 9 N 5
1203128 9 N NA 9 N 5
1203129 9 N NA 9 N 5
1203222 9 N NA 9 N 5
1203225 9 N NA 9 N 5
temp temp.qc dew.point dew.point.qc atm.press atm.press.qc rh
1203053 -17.2 5 NA 9 NA 9 NA
1203055 -17.2 5 NA 9 NA 9 NA
1203128 -17.2 5 NA 9 NA 9 NA
1203129 -17.2 5 NA 9 NA 9 NA
1203222 -17.2 5 NA 9 NA 9 NA
1203225 -17.2 5 NA 9 NA 9 NA
met2 variable is now assigned the ascending order of temperature values from our main dataset that are > -40C, and we notice that the minimum temperature is now -17.2C.
The location of the “suspicious” temperature is lat 38.767 and lon -104.3, which according to Google Earth is at 1838m.
Let us remove all the temperatures colder than -15 degC, and summarise the data.
met <- met[met$temp >-15, ]met2 <- met[order(met$temp), ]head(met2)
USAFID WBAN year month day hour min lat lon elev wind.dir
2370758 726764 94163 2019 8 27 11 50 44.683 -111.116 2025 NA
2370759 726764 94163 2019 8 27 12 10 44.683 -111.116 2025 NA
2370760 726764 94163 2019 8 27 12 30 44.683 -111.116 2025 NA
2370761 726764 94163 2019 8 27 12 50 44.683 -111.116 2025 NA
252489 720411 137 2019 8 18 12 35 36.422 -105.290 2554 NA
2370688 726764 94163 2019 8 26 12 30 44.683 -111.116 2025 NA
wind.dir.qc wind.type.code wind.sp wind.sp.qc ceiling.ht ceiling.ht.qc
2370758 9 C 0 5 22000 5
2370759 9 C 0 5 22000 5
2370760 9 C 0 5 22000 5
2370761 9 C 0 5 22000 5
252489 9 C 0 5 22000 5
2370688 9 C 0 5 22000 5
ceiling.ht.method sky.cond vis.dist vis.dist.qc vis.var vis.var.qc temp
2370758 9 N 16093 5 N 5 -3.0
2370759 9 N 16093 5 N 5 -3.0
2370760 9 N 16093 5 N 5 -3.0
2370761 9 N 16093 5 N 5 -3.0
252489 9 N 16093 5 N 5 -2.4
2370688 9 N 16093 5 N 5 -2.0
temp.qc dew.point dew.point.qc atm.press atm.press.qc rh
2370758 C -5.0 C NA 9 86.26537
2370759 5 -4.0 5 NA 9 92.91083
2370760 5 -4.0 5 NA 9 92.91083
2370761 C -4.0 C NA 9 92.91083
252489 5 -3.7 5 NA 9 90.91475
2370688 5 -3.0 5 NA 9 92.96690
From head(met2), we note that the new minimum temperature is a more reasonable -3 deg C.
USAFID WBAN year month
Min. :720385 Min. :419 Min. :2019 Min. :8
1st Qu.:720385 1st Qu.:419 1st Qu.:2019 1st Qu.:8
Median :720385 Median :419 Median :2019 Median :8
Mean :720385 Mean :419 Mean :2019 Mean :8
3rd Qu.:720385 3rd Qu.:419 3rd Qu.:2019 3rd Qu.:8
Max. :720385 Max. :419 Max. :2019 Max. :8
NA's :60271 NA's :60271 NA's :60271 NA's :60271
day hour min lat
Min. : 1.0 Min. : 0.00 Min. : 6.00 Min. :39.8
1st Qu.: 8.0 1st Qu.: 6.00 1st Qu.:13.00 1st Qu.:39.8
Median :16.0 Median :12.00 Median :36.00 Median :39.8
Mean :16.1 Mean :11.66 Mean :34.38 Mean :39.8
3rd Qu.:24.0 3rd Qu.:18.00 3rd Qu.:53.00 3rd Qu.:39.8
Max. :31.0 Max. :23.00 Max. :59.00 Max. :39.8
NA's :60271 NA's :60271 NA's :60271 NA's :60271
lon elev wind.dir wind.dir.qc
Min. :-105.8 Min. :4113 Min. : 10.0 Length:62388
1st Qu.:-105.8 1st Qu.:4113 1st Qu.:250.0 Class :character
Median :-105.8 Median :4113 Median :300.0 Mode :character
Mean :-105.8 Mean :4113 Mean :261.5
3rd Qu.:-105.8 3rd Qu.:4113 3rd Qu.:310.0
Max. :-105.8 Max. :4113 Max. :360.0
NA's :60271 NA's :60271 NA's :60508
wind.type.code wind.sp wind.sp.qc ceiling.ht
Length:62388 Min. : 0.00 Length:62388 Min. : 30
Class :character 1st Qu.: 4.10 Class :character 1st Qu.: 2591
Mode :character Median : 6.70 Mode :character Median :22000
Mean : 7.24 Mean :15145
3rd Qu.: 9.80 3rd Qu.:22000
Max. :21.10 Max. :22000
NA's :60439 NA's :60275
ceiling.ht.qc ceiling.ht.method sky.cond vis.dist
Min. :5.00 Length:62388 Length:62388 Min. : 0
1st Qu.:5.00 Class :character Class :character 1st Qu.:16093
Median :5.00 Mode :character Mode :character Median :16093
Mean :5.01 Mean :15913
3rd Qu.:5.00 3rd Qu.:16093
Max. :9.00 Max. :16093
NA's :60271 NA's :60954
vis.dist.qc vis.var vis.var.qc temp
Length:62388 Length:62388 Length:62388 Min. : 1.00
Class :character Class :character Class :character 1st Qu.: 6.00
Mode :character Mode :character Mode :character Median : 8.00
Mean : 8.13
3rd Qu.:10.00
Max. :15.00
NA's :60271
temp.qc dew.point dew.point.qc atm.press
Length:62388 Min. :-6.00 Length:62388 Min. : NA
Class :character 1st Qu.: 0.00 Class :character 1st Qu.: NA
Mode :character Median : 0.00 Mode :character Median : NA
Mean : 0.87 Mean :NaN
3rd Qu.: 2.00 3rd Qu.: NA
Max. : 7.00 Max. : NA
NA's :60271 NA's :62388
atm.press.qc rh
Min. :9 Min. :53.63
1st Qu.:9 1st Qu.:58.10
Median :9 Median :61.39
Mean :9 Mean :60.62
3rd Qu.:9 3rd Qu.:61.85
Max. :9 Max. :70.01
NA's :60271 NA's :60271
cor(elev$temp, elev$wind.sp, use="complete.obs")
[1] -0.09373843
cor(elev$temp, elev$hour, use="complete.obs")
[1] 0.4397261
cor(elev$wind.sp, elev$day, use="complete.obs")
[1] 0.3643079
cor(elev$wind.sp, elev$hour, use="complete.obs")
[1] 0.08807315
cor(elev$temp, elev$day, use="complete.obs")
[1] -0.003857766
Correlations: Temperature and wind speed have a very weak inverse relationship Temperature and hours have a moderately positive correlation Wind speed and day also have a moderately positive correlation Wind speed and day have a very weak relationship Teperature and day also have no meaningful linear relationship
Part 3: Exploratory Graphs
hist(met$elev, breaks=100)
hist(met$temp)
hist(met$wind.sp)
Elevation is very skewed to the right, most stations are at low to moderate altitudes. Temperature (after cleaning) shows a normal distribution Windspeed is also right skewed, and many light-wind hours are observed.
Where is the station with the highest elevation located?
library(leaflet)library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.2 ✔ tibble 3.2.1
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Min. 1st Qu.
"2019-08-01 00:00:00.0000" "2019-08-08 11:00:00.0000"
Median Mean
"2019-08-16 22:00:00.0000" "2019-08-16 14:09:56.8823"
3rd Qu. Max.
"2019-08-24 11:00:00.0000" "2019-08-31 22:00:00.0000"
NA's
"60271"
elev <- elev[order(date)]head(elev)
USAFID WBAN year month day hour min lat lon elev wind.dir
<int> <int> <int> <int> <int> <int> <int> <num> <num> <int> <int>
1: 720385 419 2019 8 1 0 36 39.8 -105.766 4113 170
2: 720385 419 2019 8 1 0 54 39.8 -105.766 4113 100
3: 720385 419 2019 8 1 1 12 39.8 -105.766 4113 90
4: 720385 419 2019 8 1 1 35 39.8 -105.766 4113 110
5: 720385 419 2019 8 1 1 53 39.8 -105.766 4113 120
6: 720385 419 2019 8 1 2 12 39.8 -105.766 4113 120
wind.dir.qc wind.type.code wind.sp wind.sp.qc ceiling.ht ceiling.ht.qc
<char> <char> <num> <char> <int> <int>
1: 5 N 8.8 5 1372 5
2: 5 N 2.6 5 1372 5
3: 5 N 3.1 5 1981 5
4: 5 N 4.1 5 2134 5
5: 5 N 4.6 5 2134 5
6: 5 N 6.2 5 22000 5
ceiling.ht.method sky.cond vis.dist vis.dist.qc vis.var vis.var.qc temp
<char> <char> <int> <char> <char> <char> <num>
1: M N NA 9 N 5 9
2: M N NA 9 N 5 9
3: M N NA 9 N 5 9
4: M N NA 9 N 5 9
5: M N NA 9 N 5 9
6: 9 N NA 9 N 5 9
temp.qc dew.point dew.point.qc atm.press atm.press.qc rh
<char> <num> <char> <num> <int> <num>
1: 5 1 5 NA 9 57.61039
2: 5 1 5 NA 9 57.61039
3: 5 2 5 NA 9 61.85243
4: 5 2 5 NA 9 61.85243
5: 5 2 5 NA 9 61.85243
6: 5 2 5 NA 9 61.85243
date
<POSc>
1: 2019-08-01 00:00:00
2: 2019-08-01 00:00:00
3: 2019-08-01 01:00:00
4: 2019-08-01 01:00:00
5: 2019-08-01 01:00:00
6: 2019-08-01 02:00:00
Now with the date-time variable, we plot the time series of temperature and wind speed
plot(elev$date, elev$temp, type='l')
plot(elev$date, elev$wind.sp, type='l')
To summarize this data as visualized in the above plots:
I see that the station with max elevation has a pretty cyclical temperature fluctuation every day, but there seems to be a very noticeable spike in wind speeds mid-August, possibly as the Fall season sets in.
A question that I would like to ask, and build an exploratory plot for: Which hours have the most data? For this, I could plot the frequency by hour, and use a barplot to visualize-
barplot(table(elev$hour))
Looking at ?barplot, I see that I can use xlab and ylab to label the x and y axes respectively.
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